An alternate method that avoids these limitations is DIP [1]}, which attempts to generate the image from randomly initialized weights. Since all neurons participate in forward inference, DIP is much faster than S2S. However, DIP quickly starts overfitting the data, and it is not trivial to establish convergence criteria. Another alternative is N2F [2]}, which maps downsamplings of the image to one another. N2F is by far the fastest blind zero-shot denoiser, however, accuracy pales in comparison to S2S.
